Solar Flare Forecast Model Based on Resampling and Fusion Method

Predicting the activity of solar flares is of great significance for studying its physical mechanism and the impact on human production and life.Problems such as class imbalance,high time-series sensitivity,and over-localization of important features exist in the sample data used for flare forecasting.We design a solar flare fusion method based on resampling and the CNN-GRU algorithm to try to solve the above problems.In order to verify the effectiveness of this fusion method,first,we compared the forecast performance of different resampling methods by keeping the forecast model unchanged.Then,we used the resampling algorithm with high performance to combine some single forecast models and fusion forecast models respectively.We use the 2010-2017 sunspot data set to train and test the performance of the flare model in predicting flare events in the next 48 h.Through the conclusion of the above steps,we prove that the resampling method SMOTE and its variant SMOTE-ENN are more advantageous in class imbalance problem of flare samples.In addition,after the fusion of one-dimensional convolution and recurrent network with"forget-gate",combined with the SMOTE-ENN to achieve TSS=61%,HSS=61%,TP_(Rate)=77%and TN_(Rate)=83%.This proves that the fusion model based on resampling and the CNN-GRU algorithm is more suitable for solar flare forecasting.

Flare Forecast Model Based on DS-SMOTE and SVM with Optimized Regular Term

The research of flare forecast based on the machine learning algorithm is an important content of space science.In order to improve the reliability of the data-driven model and weaken the impact of imbalanced data set on its forecast performance,we proposes a resampling method suitable for flare forecasting and a Particle Swarm Optimization(PSO)-based Support Vector Machine(SVM)regular term optimization method.Considering the problem of intra-class imbalance and inter-class imbalance in flare samples,we adopt the density clustering method combined with the Synthetic Minority Over-sampling Technique(SMOTE)oversampling method,and performs the interpolation operation based on Euclidean distance on the basis of analyzing the clustering space in the minority class.At the same time,for the problem that the objective function used for strong classification in SVM cannot adapt to the sample noise,In this research,on the basis of adding regularization parameters,the PSO algorithm is used to optimize the hyperparameters,which can maximize the performance of the classifier.Finally,through a comprehensive comparison test,it is proved that the method designed can be well applied to the flare forecast problem,and the effectiveness of the method is proved.

Research Progress on Solar Flare Forecast Methods Based on Data-driven Models

Eruption of solar flares is a complex nonlinear process,and the rays and high-energy particles generated by such an eruption are detrimental to the reliability of space-based or ground-based systems.So far,there are not reliable physical models to accurately account for the flare outburst mechanism,but a lot of data-driven models have been built to study a solar flare and forecast it.In the paper,the status of solar-flare forecasting is reviewed,with emphasis on the machine learning methods and data-processing techniques used in the models.At first,the essential forecast factors strongly relevant to solar flare outbursts,such as classification information of the sunspots and evolution pattern of the magnetic field,are reviewed and analyzed.Subsequently,methods of resampling for data preprocessing are introduced to solve the problems of class imbalance in the solar flare samples.Afterwards,typical model structures adopted for flare forecasting are reviewed from the aspects of the single and fusion models,and the forecast performances of the different models are analyzed.Finally,we herein summarize the current research on solar flare forecasting and outline its development trends.

Subintimal recanalization for non-acute occlusion of intracranial vertebral artery in an emergency endovascular procedure:A case report

BACKGROUND Endovascular recanalization of non-acute intracranial artery occlusion is technically difficult,particularly when the microwire enters the subintima.Although the subintimal tracking and re-entry technique has been well established in the endovascular treatment of coronary artery occlusion,there is limited experience with its use in intracranial occlusion due to anatomical variations and a lack of dedicated devices.CASE SUMMARY A 74-year-old man was admitted to the hospital two days after experiencing acute weakness in both lower extremities,poor speech,and dizziness.After admission,imaging revealed acute ischemic stroke and non-acute occlusion of bilateral intracranial vertebral arteries(ICVAs).On the fourth day of admission,the patient's condition deteriorated and an emergency endovascular recanalization of the left ICVA was performed.During this procedure,a microwire was advanced in the subintima of the vessel wall and successfully reentered the distal true lumen.Two stents were implanted in the subintima.The patient's Modified Rankin Scale was 1 at three months postoperatively.CONCLUSION We present a technical case of subintimal recanalization for non-acute ICVA occlusion in an emergency endovascular procedure.However,we emphasize the necessity for caution when applying the subintimal tracking approach in intracranial occlusion due to the significant dangers involved.

Class imbalance problem in short-term solar flare prediction

Using data-driven algorithms to accurately forecast solar flares requires reliable data sets.The solar flare dataset is composed of many non-flaring samples with a small percentage of flaring samples.This is called the class imbalance problem in data mining tasks.The prediction model is sensitive to most classes of the original data set during training.Therefore,the class imbalance problem for building up the flare prediction model from observational data should be systematically discussed.Aiming at the problem of class imbalance,three strategies are proposed corresponding to the data set,loss function,and training process:TypeⅠresamples the training samples,including oversampling for the minority class,undersampling,or mixed sampling for the majority class.TypeⅡusually changes the decision-making boundary,assigning the majority and minority categories of prediction loss to different weights.TypeⅢassigns different weights to the training samples,the majority categories are assigned smaller weights,and the minority categories are assigned larger weights to improve the training process of the prediction model.The main work of this paper compares these imbalance processing methods when building a flare prediction model and tries to find the optimal strategy.Our results show that among these strategies,the performance of oversampling and sample weighting is better than other strategies in most parameters,and the generality of resampling and changing the decision boundary is better.